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Article

Responses of the Leaf Water Physiology and Yield of Grapevine via Different Irrigation Strategies in Extremely Arid Areas

by
Ruifeng Sun
,
Juanjuan Ma
*,
Xihuan Sun
,
Lijian Zheng
and
Jiachang Guo
College of Water Resource Science and Engineering, Taiyuan University of Technology, Taiyuan 030024, China
*
Author to whom correspondence should be addressed.
Sustainability 2023, 15(4), 2887; https://doi.org/10.3390/su15042887
Submission received: 2 January 2023 / Revised: 28 January 2023 / Accepted: 3 February 2023 / Published: 6 February 2023
(This article belongs to the Special Issue Sustainable Water-Saving Irrigation)
Browse Figures
Versions Notes

Abstract

:
The contradiction between water supply and demand has become increasingly prominent due to the large agricultural water consumption and low irrigation water use efficiency (IWUE) in the extremely arid area of Xinjiang, which needs to be solved by efficient irrigation. In this study, the effects of different irrigation levels (the lower and upper limits of irrigation (LULI) were 50–80%, 60–90%, and 70–100% of the field capacity (FC), respectively) under two irrigation methods (root zone irrigation (RZI) and furrow irrigation (FI)) on the photosynthetic physiology and yield of grape were analyzed to explore suitable irrigation schemes in extremely arid areas. The results show that the diurnal variation curve of the net photosynthetic rate (Pn) of grape leaves in the extreme arid region was not sensitive to the response of irrigation methods. However, RZI could improve the apparent quantum efficiency and maximum photosynthetic rate by 60.00% and 31.25%, respectively, reduce the light compensation point by 17.91%, and alleviate the photosynthetic lunch break phenomenon. Under FI, the physiological indexes of leaves increased with the increase in the LULI, while the Pn and SPAD values were the largest under RZI when the LULI was 60–90% of FC. The daily average Pn value of T2 in 2021 and 2022 ranged from 12.93 to 17.77 μmol·m−2·s−1. Compared with FI, RZI significantly improved the leaf water potential, Pn, and SPAD values by increasing the soil water content (SWC) of the 40–80 cm soil layer by 5.04–8.80%, which increased the yield by 6.86–18.67%. The results show that the yield and water use efficiency reached the peak when the LULI was 60–90% of FC under RZI, which could provide theoretical support for efficient irrigation of vineyards in extremely arid areas.

1. Introduction

Xinjiang is one of the well-known grape industry bases [1]. In Xinjiang’s extremely arid regions, irrigation is an important source of water during the grape growing season. The lack of reasonable irrigation measures has led to serious water waste and low water use efficiency (WUE) in the region [2]. Therefore, it is of great significance to carry out efficient water-saving irrigation in Xinjiang. Previous studies have shown that drip irrigation can effectively improve the irrigation efficiency and WUE [3]. However, the extremely arid area has the climatic characteristics of large soil evaporation potential, and the root distribution of fruit trees is deep, while the soil water distribution of conventional drip irrigation is concentrated in the 0–40 cm soil layer with fewer roots in the middle and deep soil layer [4], which is not conducive to improving the water use in the middle and deep soils. Studies have shown that deficit irrigation performs better when seasonal rainfall is greater than 200 mm [5], but the scarcity of average annual rainfall in extremely arid areas has affected the irrigation effectiveness of many irrigation techniques in the region. Therefore, it is of great significance to seek irrigation technologies suitable for fruit trees in extremely arid areas to alleviate the contradiction between the supply and demand of water resources in this region.
Irrigation strategies can change the distribution of soil water and roots, and then regulate the physiological activities of water in plant leaves [6]. The leaf water potential (Ψ) and SPAD are indicators used to study leaf water status and physiological activities, and their changes will significantly affect photosynthesis [7]. Irrigation methods and the SWC will affect the physiological indicators of leaves [8,9]. When the SWC decreases due to deficit irrigation, the Ψ and chlorophyll content may decrease, and the physiological activities of leaves may be restricted [10,11]. Studies have shown that increasing the Ψ and chlorophyll content can promote photosynthesis and increase yield [12]. Scientific and reasonable irrigation can keep the leaf water potential and SPAD at a high level [13,14]. However, the physiological activities of leaves respond to irrigation differently under different climatic conditions [6,15]. Therefore, studying the relevant parameters affecting leaf photosynthesis in extreme arid areas can provide a scientific basis for the formulation of irrigation schemes.
Photosynthesis is the main source of organic matter during the growth period of fruit trees, which is essential for canopy growth and yield formation [16,17]. The daily variation in the Pn can reflect the continuous ability of photosynthesis in a day. Under different climatic conditions, meteorological factors such as air temperature, air humidity, and photosynthetic effective radiation have different influences on the value of the Pn, and the daily variation in the Pn will show a unimodal or bimodal curve [18,19]. The Pn represents the strength of photosynthesis, and irrigation can increase the parameters of the light response curve such as the maximum net photosynthetic rate and the light saturation point, which are conducive to improving the Pn [20]. Studies have found that the Pn of underground irrigation can be increased by 42% compared to surface irrigation [21]. At the same time, the SWC is also an important factor affecting photosynthesis, but there are differences in the response of photosynthesis to the SWC. Studies have shown that water stress reduces the Pn of leaves [22]. However, other studies have indicated that deficit drip irrigation can increase the net photosynthetic rate by about 25% and maintain a high yield [23]. Overall, improving leaf photosynthesis through irrigation is conducive to the accumulation of organic matter in fruit trees and increases yield. Climatic conditions, irrigation methods, and soil moisture content all have an impact on photosynthesis. Therefore, it is necessary to evaluate the applicability of irrigation methods and suitable irrigation schemes for photosynthesis under specific climatic conditions in order to efficiently achieve save water irrigation and improve fruit yield.
Based on the previous studies and the extreme arid climate conditions, a new root zone irrigation (RZI) method suitable for perennial fruit trees was introduced, and the irrigation water infiltrated into the soil through the irrigation emitter arranged around the grapevine, which was conducive to reducing the SWC of the surface soil. Meanwhile, the irrigation method was simple, and its irrigation system had a long service life. However, the effect of RZI on the physiological activity of leaves and yield is not clear. So, we analyze the water physiology indexes of grape leaves in the extremely arid region under RZI, as well as the response relationship between yield and irrigation. The objectives of this study include the following: (1) to compare the characteristics of SWC distribution under different irrigation strategies; analyze the relationship between the irrigation strategies and the Ψ, SPAD, and photosynthesis; and reveal the influence mechanism of irrigation mode on leaf water physiology of grapevine; and (2) to study the diurnal variation characteristics of the Pn and Tr of grape leaves, and determine the appropriate irrigation scheme in the extremely arid area of Xinjiang based on the WUE of different scales and yields.

2. Materials and Methods

2.1. Experimental Site

The study was carried out at the test base (90.30° E, 42.91° N, altitude 419 m) of the Xinjiang Grape and Fruit Development Research Center in Shanshan County, Xinjiang, in 2021 and 2022. The average annual rainfall in this area was 25.3 mm, and the average annual evaporation was 2751 mm. The effective accumulated temperature above 10 °C exceeded 4525 °C. The annual sunshine time was 2900–3100 h, and the frost-free period was over 192 days. The test material was a 6-year-old seedless white grape vine, with E-W row orientation. The spacing of the vines was 2 m, and the row spacing was 4 m. The orchard was planted by furrow and ridge, and the furrow width was 1.2 m. The soil texture was loamy, the average dry bulk density of the 0–130 cm soil was 1.53 g·cm−3, and the average field capacity was 26%. Figure 1 show the air temperature, relative humidity, and rainfall data in the experimental area. In 2021, the automatic meteorological monitoring station of the orchard was used to collect relevant meteorological index data, and in 2022, the meteorological data were obtained from the meteorological station in the experimental area.

2.2. Treatments and Irrigation Management

The irrigation methods in this study included FI and RZI. FI directly transported water into the furrow, and irrigation water infiltrated into the soil from the surface. RZI was used to transport water to the irrigation emitter through a pipe, and then the irrigation water infiltrated into the soil through the emitter. Four emitters were arranged symmetrically under each grapevine. The emitter was a cylindrical structure with a diameter of 20 cm and a depth of 40 cm, with the top and bottom sealed and impervious, and the sidewalls seeping water. The center of the irrigation emitter was 50 cm away from the grapevine, and the field layout diagram was shown in Figure 2.
The irrigation method and the lower and upper limits of irrigation (LULI) were the variables of this study, which included six treatments with three replicates for each treatment. T1 (50–80% of FC), T2 (60–90% of FC), and T3 (70–100% of FC) were the RZI treatments, and CK1 (50–80% of FC), CK2 (60–90% of FC), and CK3 (70–100% of FC) were the FI treatments. Table 1 show the irrigation amounts and frequencies in different growth stages of each treatment. According to local standards, 120 kg·hm−2 of urea was applied as the base fertilizer at the early growth stage, 225 kg·hm−2 of urea and 185 kg·hm−2 of diammonium phosphate were applied before anthesis, 150 kg·hm−2 of urea and 190 kg·hm−2 of diammonium phosphate were applied after anthesis, and 90 kg·hm−2 of potassium sulfate was applied at the maturity stage. Fertilization measures were the same in all treatments. Other management practices, such as pruning, flower thinning, and fruit thinning, were consistent with the orchard.

2.3. Measurement Indices and Methods

2.3.1. Soil Moisture

The soil water content (SWC) was monitored using the TRIME-PICO-IPH soil moisture measurement system (TDR). Three trees from each treatment were selected to measure soil moisture. The SWC measurement points of the two irrigation methods were the same, and the distribution of measurement points is shown in Figure 2. The measuring depth was 100 cm, and the data were recorded at intervals of 20 cm. The SWC was measured once every 5 days. Additional measurements were carried out after 1 day of irrigation and precipitation.

2.3.2. Leaf Water Potential and SPAD

For each treatment, three healthy leaves with no pests and diseases were selected, and the leaf water potential was measured every 2 h from 8:00 to 20:00 using a dew point water potential meter (WP4C, Decagon Devices, Inc., Pullman, WA, USA) on sunny days during the shoot growth stage, the fruit expansion stage, and the mature stage.
The leaf SPAD values were measured using a portable SPAD instrument (SPAD-502, Konica Minolta) every 5 days during rapid leaf growth and every 10 days during fruit expansion and maturity.

2.3.3. Photosynthetic Data

The leaf net photosynthetic rate and transpiration rate were determined every 2 h using a portable photosynthesis system (LI-6400, Li-Cor, USA) on sunny days from 8:00 to 20:00 during each growth period of the vine. Three leaves with the same maturity and no pests and diseases were selected for determination in each treatment. At the end of the mature stage, the light response curves of T2 and CK2 were determined on sunny days and fitted using the rectangular hyperbolae model. The light compensation point (Ic) was calculated by equations.
The rectangular hyperbolae model is
P n = α I P m a x α I + P m a x − R d
where Pn is the net photosynthetic rate, μmol·m−2·s−1; I is the photosynthetically active radiation, μmol·m−2·s−1; α is the apparent quantum efficiency; Pmax is the maximum photosynthetic rate, μmol·m−2·s−1; and Rd is the dark respiration rate, μmol·m−2·s−1.

2.3.4. Yield

Grapes were harvested at the end of the mature stage, and the yield of each treatment was measured. The irrigation water use efficiency was calculated by the yield and the total irrigation amount, and the leaf water use efficiency was calculated by the average net photosynthetic rate and transpiration rate during the growing period.

2.4. Data Processing and Analysis

Analysis of variance (ANOVA) and correlation analysis were performed using the software package SPSS (IBM SPSS Statistics 26, SPSS Inc., Chicago, IL, USA). The Duncan test was used to compare means, and differences were considered significant at a level of p < 0.05. The correlation between the SWC and physiological indicators was analyzed using the correlation plot of the Origin2022 software.

3. Results

3.1. Soil Moisture

The distribution of the two-dimensional SWC under different treatments after 1 day of irrigation is shown in Figure 3. In the vertical direction, the SWC under FI decreased with an increase in soil depth, and the SWC of the 0–40 cm soil layer was 22.22–26.30%. However, with the increase in soil depth, the SWC under RZI increased first and then decreased. The SWC of the 40–60 cm soil layer under RZI was the largest, and the SWC of the 40–80 cm soil layer was 21.81–25.65%, which was 5.04–8.80% higher than that of the 40–80 cm soil layer under FI. The SWC of the 0–20 cm soil layer under RZI was 7.35–14.49% lower than that under FI. In the horizontal direction, soil moisture was mainly concentrated in the irrigation furrows after FI, and the coefficient of variation (CV) of the SWC at each measurement point was 0.12–0.17. However, the CV of the SWC under RZI was only 0.02–0.08. Compared with furrow irrigation, RZI can improve the SWC of the middle and deep soil layers and the irrigation uniformity, which was conducive to reducing water evaporation loss and expanding the water absorption range of roots.

3.2. Leaf Physiology

3.2.1. Leaf Water Potential

The daily average leaf water potential (Ψ) in different growth stages is shown in Figure 4. During the same growth stage, the Ψ of different treatment was significantly different (p < 0.05). There was no significant difference in the Ψ in 2021 and 2022 for each treatment. When the LULI was the same, the Ψ values under RZI were 24.24%, 16.07%, and 17.20% higher than those under FI during the shoot growth stage, the fruit expansion stage, and the mature stage, respectively. The higher value for the LULI, the greater the Ψ. The Ψ of T3 under RZI was the highest. The average Ψ values of T3 in 2021 and 2022 were −0.78 Mpa and −0.72 Mpa, respectively, which were 12.36% and 29.73%, and 13.25% and 30.77% higher, respectively, than those of T2 and T1. However, under FI conditions, the average Ψ values of CK3 were the largest, i.e., −0.99 Mpa and −0.91 Mpa in 2021 and 2022, respectively, which were 10.00% and 25.56%, and 11.65% and 27.20% higher, respectively, than those of CK2 and CK1. In addition, with the change in air temperature and leaf maturity, the Ψ of the same treatment in different growth stages was also different. The Ψ was the highest in the shoot growth stage, which reached significant levels (p < 0.05) compared with the fruit expansion stage and the mature stage. However, there was no significant difference in the Ψ between the fruit expansion stage and the mature stage.

3.2.2. SPAD

During the growth period, the SPAD value showed a trend of rapid growth and then slow growth or decline (Figure 5). The leaf chlorophyll accumulated rapidly in the shoot growth stage and the growth rate of SPAD under RZI was higher than that under FI. The daily growth rates of SPAD under RZI and FI in the shoot growth stage were 0.28–0.35 and 0.21–0.31, respectively. Among them, the daily growth rate of T2 treatment was the largest, but there was less difference between T3 treatment and T2 treatment. The daily growth rate of CK1 was significantly lower than that of other treatments. The SPAD of FI treatment gradually decreased during the mature stage, while the accumulation rate of SPAD in RZI treatment was 0.01–0.04. The results show that the low leaf water potential of FI affected the water status and nutrient absorption capacity of leaves, which made leaves appear aging in the mature stage in advance, while RZI could delay leaf aging. When the LULI was the same, the maximum SPAD values of RZI during the growth period in 2021 and 2022 were 6.63–10.25% and 5.39–7.83% higher than those of FI, respectively. The SPAD values for T2 under RZI were 44.50 and 44.26 in 2021 and 2022, respectively, which were 1.21% and 8.73% higher, respectively, than those of T3 and T1. However, under FI conditions, the SPAD value decreased with the decrease in the LULI, and the maximum SPAD values of CK3 were 41.29 and 41.44 in 2021 and 2022, respectively.

3.2.3. Net Photosynthetic Rate

The diurnal variation curves of net photosynthetic rates (Pn) in different growth stages are shown in Figure 6. The daily variation curve of Pn in the shoot growth stage was a unimodal curve, and the peak of the Pn in all treatments appeared at 12:00. However, the Pn during the fruit expansion stage and the mature stage presented a photosynthetic lunch break phenomenon under the influence of high-temperature weather, and the daily variation in the Pn showed an asymmetric bimodal curve, with peaks appearing at 12:00 and 18:00, and the photosynthetic lunch break phenomenon occurred at 14:00 and 16:00. In 2021 and 2022, the daily peak of the Pn under RZI was 16.40–23.59 μmol·m−2·s−1, while that under FI was 13.16–21.41 μmol·m−2·s−1. Under RZI, the Pn of T2 treatment was the largest, and the daily average values were 30.88% and 3.04% higher than those of T1 and T3 treatments, respectively. However, the Pn of CK3 treatment was the largest under FI, and the daily average values were 46.46% and 6.00% higher than those of CK1 and CK2 treatments, respectively. There were differences in the reduction degree of the Pn caused by the photosynthetic lunch break phenomenon under different irrigation strategies. The weakest degree of photosynthetic lunch break was in the T3 treatment, and the Pn values of the fruit expansion stage and mature stage at 16:00 were 29.40% and 34.36% lower than that at 12:00, respectively, while the largest degree of photosynthetic lunch break was in the CK1 treatment, and the Pn values at 16:00 in the fruit expansion stage and mature stage were 40.58% and 48.46% lower than those at 12:00, respectively.

3.2.4. Transpiration Rate of Leaves

The daily variation in the transpiration rate (Tr) in different growth stages showed a unimodal curve that first increased and then decreased (Figure 7). The Tr of the shoot growth stage and the fruit expansion stage reached the maximum at 14:00, while the Tr reached the maximum at 12:00 in the mature stage. When the LULI was the same, the average Tr of RZI during the whole growth period was 2.45–21.34% higher than that of FI. The higher the LULI, the greater the Tr of the leaves. When the LULI was 50–80% of FC, the Tr would be significantly reduced. In 2021 and 2022, the average daily Tr of T3 treatment was 2.42%~5.87% and 39.65%~50.91% higher than that of T2 and T1 treatments, respectively. The daily average Tr of CK3 treatment was 3.45%~12.50% and 58.18%~82.59% higher than that of CK2 and CK1 treatments, respectively.

3.3. Light Response Curve

The rectangular hyperbolae model can better simulate the response of the Pn to light intensity under RZI and FI (Figure 8). According to the model calculation results (Table 2), the α and Pmax values of RZI were 60.00% and 31.25% higher than those of FI, respectively, while Ic was 17.91% lower than that of FI. The results show that the utilization capacity of RZI for weak light and strong light was higher than that of FI. Meanwhile, Rd of RZI was 23.94% higher than that of FI. The results show that the physiological activity capacity of leaves under RZI was greater than that of FI.

3.4. Correlation Analysis between Soil Moisture and Leaf Physiology

According to the correlation analysis results between the SWC in different soil layers and physiological indexes (Figure 9), the correlation between the SWC of the 0–20 cm soil layer and leaf physiological indexes was not significant, indicating that a large amount of evaporation loss would occur in the surface soil under the influence of high-temperature weather in extreme arid areas, which reduced the correlation between the SWC of surface soil and the physiological activities of grape leaves. Overall, the correlation between the SWC of the 20–80 cm soil layer and leaf physiological indexes was high, and the SWC of the 40–80 cm soil layer reached a significant correlation level with all indexes, indicating that the water and nutrient supply of middle and deep soil were important guarantees for leaf physiological activities. There was a significant correlation between the Ψ, Pn, and Tr values. Therefore, maintaining a high Ψ was crucial for maintaining leaf photosynthesis.

3.5. Yield, IWUE, and WUEl

There were significant differences in the yield, irrigation water use efficiency (IWUE), and leaf water use efficiency (WUEl) for different treatments (Figure 10). Due to the significant influence of the irrigation mode on leaf water physiological indexes, correlation analysis was conducted on leaf water physiological indexes and yield (Table 3), and the results show that Ψ, Pn, and SPAD values were highly significantly correlated with the yield (p < 0.01). Compared with FI, RZI increased the yield by 6.86–18.67% when the LULI was the same. In this study, average temperature in May 2022 was 27.68°C, 14.85% higher than in 2021, resulting in a lower fruit setting rate in 2022, which, in turn, made the yield lower than in 2021. However, the yield relationship between different irrigation treatments remained consistent in 2021 and 2022. The yields of T2 treatment were the largest in 2021 and 2022, at 28,464.38 kg·hm−2 and 24829.17 kg·hm−2, respectively. The IWUE and WUEl under RZI were 6.08 kg·m−3–8.24 kg·m−3 and 2.80 μmol·mmol–3.30 μmol·mmol, respectively, which were 27.40–44.57% and 5.59–16.86% higher, respectively, than those under FI. The responses of the IWUE and WUEl to the LULI were different. Under RZI and FI conditions, when the LULI was 60–90% of the FC, the IWUE was the greatest. The WUEl was negatively correlated with the LULI, and the WUEl was the greatest when the LULI was 50–80%, but the difference between the WUEl of T2 and T1 under RZI was not significant.

4. Discussion

The soil water distribution was significantly affected by different irrigation schemes [24]. Soil moisture was mainly concentrated in the surface soil under FI. The higher value for the LULI, the greater the SWC of the surface soil. Since soil evaporation loss mainly depends on the surface soil water [25], FI can cause a large amount of evaporation loss in arid and extreme arid areas where evaporation potential is large [26]. However, RZI in this study reduced the SWC of the surface soil, which can effectively reduce soil evaporation loss. Studies have shown that increasing the SWC in the middle and deep soil can increase the absorption capacity of roots, thereby regulating the water status and physiological activities of fruit trees [27,28]. Compared with FI, RZI could improve the SWC of the 40–80 cm soil layer and the irrigation uniformity of the root growth area, which was conducive to improving the physiological activities of leaves.
Proper irrigation is essential for leaf physiology. In this study, the Ψ was positively correlated with the LULI, indicating that the greater the SWC, the higher the Ψ [29]. When the Ψ is maintained at a suitable level, it is conducive to the opening of stomata and the improvement of the Pn [30]. Studies have shown that deficit irrigation reduces the Ψ, chlorophyll content, and photosynthesis [31]. However, some studies have pointed out that appropriately reducing the LULI can make leaf photosynthesis occur at a high level [32]. This is because there are differences in the distribution of water and roots in soil profiles under different irrigation methods, which affect the water absorption rate of roots and change the water status and physiological activities of the leaves of fruit trees. In this study, grape leaf physiology was significantly correlated with the SWC of the 40–80 cm soil layer in the extremely arid region (Figure 9). Compared with FI, RZI increased the SWC of the 40–80 cm soil layer by 5.04–8.80% and optimized the physiological activities of leaves. The results show that increasing the SWC of middle and deep soil had a positive effect on the physiology of fruit leaves [33]. The response of the SPAD and Pn to the LULI was different under different irrigation methods. The Ψ, SPAD, and Pn values of leaves under FI decreased with the decrease in the LULI. However, when the LULI under RZI was 60–90% of the FC, the SPAD and Pn values were kept at a high level without significantly reducing the Ψ. This is because appropriately reducing the SWC of the surface soil layer can improve the permeability of the soil, which is conducive to improving the root activity capacity of the middle and deep soil layer and providing sufficient water and nutrients for the physiological activities of fruit trees [34].
The diurnal variation in Pn and Tr values can reflect the response of photosynthesis to irrigation and environmental factors [35]. Due to the different sensitivity of different crops to environmental factors, the daily variation in the Pn of fruit trees such as grapes and apples generally presents as unimodal or bimodal curves [36,37]. The diurnal variation in the Tr can generally be characterized by air temperature and solar radiation, and the Tr presents a unimodal curve or a multimodal curve [38]. In this study, the daily variation in the Tr was a unimodal curve, the photosynthetic lunch break phenomenon occurred in all treatments at the high-temperature stage, and the diurnal variation in the Pn of grape leaves at the fruit expansion stage and the mature stage showed a bimodal curve. The results show that changing the irrigation mode in the extreme arid region would not eliminate the photosynthetic lunch break phenomenon. However, due to the positive correlation between the Ψ and stomatal conductance [39], the higher the Ψ under RZI can reduce the degree of stomatal closure in the photosynthetic lunch break stage, which ensures the gas exchange capacity of the leaves in the high-temperature stage, thereby reducing the degree of photosynthetic lunch break. Light response curves can be used to quantitatively study the response of the Pn to light intensity [40]. Compared with FI, RZI reduced the Ic and increased the α and Pmax of grapes. Therefore, RZI could improve the light energy utilization capacity and photosynthesis of grapes.
Leaf water status and photosynthesis have a significant impact on yield, and fruit yield can be increased by increasing the Ψ, Pn, and chlorophyll content through reasonable irrigation [41]. In this study, compared with FI, RZI increased the SWC in the middle and deep soil and significantly increased the Ψ and SPAD, which created favorable conditions for leaf photosynthesis, and then increased the yield by 6.86–18.67%. In addition to irrigation methods, the SWC also has a significant impact on yield [42]. In general, increasing the LULI can increase the SWC, which is conducive to the increasing fruit yield. However, some studies have shown that appropriately reducing the LULI is conducive to increasing yield [43,44]. The results of this study show that the yield under FI decreased with the decrease in the LULI, indicating that the degree of water stress increased with the decrease in the LULI under FI, which would reduce the yield [45]. However, the yield of T2 treatment under root zone irrigation was the highest. This is because the irrigation method changes the contribution rate of soil water in different soil layers [46]. Appropriately reducing the LULI under RZI is beneficial to improving the absorption of water and nutrients from the middle and deep soil by the roots, which can improve the photosynthetic capacity of leaves and provide security for fruit growth.
WUE is an important indicator for evaluating efficient irrigation. Studies have shown that the response relationship between the WUE and irrigation varies under different climatic conditions [47,48]. However, in general, reducing the irrigation amount without significantly reducing the yield and photosynthetic rate can effectively improve the WUE. Compared with FI, RZI reduced the amount of irrigation water, but the yield and photosynthetic rate were higher than those of FI, which improved the WUE, indicating that RZI had a better irrigation effect in extremely arid areas. The IWUE of T2 treatment was the highest when the WUEl was not significantly reduced under RZI, indicating that irrigation efficiency could be improved under RZI when the LULI was 60–90% of the FC.

5. Conclusions

In this study, the effects of different irrigation strategies on leaf water physiology and yield of vines were analyzed. The main conclusions of this study are as follows. (1) RZI increased the SWC in the 40–80 cm soil layer and improved irrigation uniformity in planned wetted bodies. The SWC of the middle and deep soil layers in the extreme arid area had a significant effect on the physiology of grape leaves. The Ψ was positively correlated with the SWC, while the relationship between Pn and SPAD values and the LULI was affected by the irrigation method. Under RZI, the maximum Pn and SPAD values were obtained when the LULI was 60–90% of the FC. (2) The diurnal variation curve of the Pn showed a unimodal curve in the shoot growth stage, and a bimodal curve in the fruit expansion stage and the mature stage. Compared with FI, RZI could increase the Pn and light response parameters, and alleviate the photosynthetic lunch break phenomenon. In this study, yield was significantly correlated with leaf physiology, and RZI significantly increased the grape yield by improving the leaf’s physiological activity. The results show that the yield and WUE under RZI were the best when the LULI was 60–90% of the FC. (3) RZI introduced in this study is an efficient irrigation method which can be used as a theoretical basis for developing efficient irrigation strategies in extreme arid areas.

Author Contributions

R.S. edited the original draft; J.M., X.S., and L.Z. reviewed and edited the draft; R.S. and J.G. conducted the field experiments; X.S. provided the funding. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the National Natural Science Foundation of China (U1803112).

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Not applicable.

Acknowledgments

Special thanks to the Xinjiang Research Institute of Water Resources and Hydropower and the Xinjiang Grape and Fruit Development Research Center for their help in the field experiments of this research.

Conflicts of Interest

The authors declare no conflict of interest.

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Figure 1. Meteorological data of the experimental area.
Figure 1. Meteorological data of the experimental area.
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Figure 2. Schematic diagram of the field layout (cm). (a) Schematic diagram of the RZI system. The soil moisture measurement points of FI were consistent with those of RZI. (b) Schematic diagram of the distribution of furrow, ridge, and grapevine in the field.
Figure 2. Schematic diagram of the field layout (cm). (a) Schematic diagram of the RZI system. The soil moisture measurement points of FI were consistent with those of RZI. (b) Schematic diagram of the distribution of furrow, ridge, and grapevine in the field.
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Figure 3. Distribution of SWCs.
Figure 3. Distribution of SWCs.
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Figure 4. Daily average leaf water potential at each growth stage where (A), (B), and (C) represent the shoot growth stage, fruit expansion stage, and mature stage, respectively, and the different letters in Figure (A) indicate that the difference reached a significant level (p < 0.05), and Figures (B,C) are the same.
Figure 4. Daily average leaf water potential at each growth stage where (A), (B), and (C) represent the shoot growth stage, fruit expansion stage, and mature stage, respectively, and the different letters in Figure (A) indicate that the difference reached a significant level (p < 0.05), and Figures (B,C) are the same.
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Figure 5. Dynamic variation in SPAD values.
Figure 5. Dynamic variation in SPAD values.
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Figure 6. Diurnal variation in the photosynthetic rate, where (a), (b), and (c) represent the shoot growth stage, fruit expansion stage, and mature stage in 2021, respectively. Meanwhile, (d), (e), and (f) represent the shoot growth stage, fruit expansion stage, and mature stage in 2022, respectively.
Figure 6. Diurnal variation in the photosynthetic rate, where (a), (b), and (c) represent the shoot growth stage, fruit expansion stage, and mature stage in 2021, respectively. Meanwhile, (d), (e), and (f) represent the shoot growth stage, fruit expansion stage, and mature stage in 2022, respectively.
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Figure 7. Diurnal variations in the transpiration rate where (a), (b), and (c) represent the shoot growth stage, fruit expansion stage, and mature stage in 2021, respectively. Meanwhile, (d), (e), and (f) represent the shoot growth stage, fruit expansion stage, and mature stage in 2022, respectively.
Figure 7. Diurnal variations in the transpiration rate where (a), (b), and (c) represent the shoot growth stage, fruit expansion stage, and mature stage in 2021, respectively. Meanwhile, (d), (e), and (f) represent the shoot growth stage, fruit expansion stage, and mature stage in 2022, respectively.
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Figure 8. Light response curve.
Figure 8. Light response curve.
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Figure 9. Correlation between the SWC and physiological indicators where 0–20, 20–40, 40–60, 60–80, and 80–100 indicate the SWCs of each soil layer. * indicates significant correlation (p < 0.05). ** indicates significant correlation (p < 0.01).
Figure 9. Correlation between the SWC and physiological indicators where 0–20, 20–40, 40–60, 60–80, and 80–100 indicate the SWCs of each soil layer. * indicates significant correlation (p < 0.05). ** indicates significant correlation (p < 0.01).
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Figure 10. Yield and water use efficiency where the different letters in the figure indicate significant differences between treatments (p < 0.05).
Figure 10. Yield and water use efficiency where the different letters in the figure indicate significant differences between treatments (p < 0.05).
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Table
Table 1. Irrigation frequency and irrigation amount in each growth period.
Table 1. Irrigation frequency and irrigation amount in each growth period.
Germination Stage 1 (m3·hm−2)Irrigation Norm (m3·hm−2)Irrigation FrequencyTotal Irrigation Amount (m3·hm−2)
Shoot Growth StageFruit Expansion StageMature Stage
2021T1700306.151523149.20
T2306.152523455.35
T3306.152634067.65
CK1390.001523820.00
CK2390.002524210.00
CK3390.002735380.00
2022T1700306.151523149.20
T2306.152533761.50
T3306.152634067.65
CK1390.001523820.00
CK2390.002524210.00
CK3390.003635380.00
1 In order to ensure the germination rate, each treatment was irrigated once at the beginning of the growth period, and the irrigation amount was 700 m3·hm−2.
Table
Table 2. Characteristic parameters of the optical response curve.
Table 2. Characteristic parameters of the optical response curve.
αPmax
(μmol·m−2·s−1)		Ic
(μmol·m−2·s−1)		Rd
(μmol·m−2·s−1)		R2
RZI0.0821.8424.611.760.99
FI0.0516.6429.981.420.99
Table
Table 3. Correlation between the yield and physiological indicators where ** in the table indicates p < 0.01.
Table 3. Correlation between the yield and physiological indicators where ** in the table indicates p < 0.01.
20212022
ΨPnSPADΨPnSPAD
Yield0.988 **0.918 **0.926 **0.987 **0.946 **0.951 **
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Sun, R.; Ma, J.; Sun, X.; Zheng, L.; Guo, J. Responses of the Leaf Water Physiology and Yield of Grapevine via Different Irrigation Strategies in Extremely Arid Areas. Sustainability 2023, 15, 2887. https://doi.org/10.3390/su15042887

AMA Style

Sun R, Ma J, Sun X, Zheng L, Guo J. Responses of the Leaf Water Physiology and Yield of Grapevine via Different Irrigation Strategies in Extremely Arid Areas. Sustainability. 2023; 15(4):2887. https://doi.org/10.3390/su15042887

Chicago/Turabian Style

Sun, Ruifeng, Juanjuan Ma, Xihuan Sun, Lijian Zheng, and Jiachang Guo. 2023. "Responses of the Leaf Water Physiology and Yield of Grapevine via Different Irrigation Strategies in Extremely Arid Areas" Sustainability 15, no. 4: 2887. https://doi.org/10.3390/su15042887

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